Model Generalization: Recently, domain adaptation and generalization are very hot topics in the natural/medical image segmentation fields [1]}, [2]}. But for the abdominal multi-organ segmentation task, there are very few studies [3]} focused on investigating the generalization problem. This is mainly due to lacking open available multi-sources and large-scale datasets/benchmarks. In this work, we investigate the domain gaps between our build WORD dataset and open-source datasets BTCV [4]} and TCIA [5]} and find that there are significant domain gaps across different source datasets. To boost the deep learning-based clinical applications, it is very desirable to train models with good generalization and high performance. So, we build a benchmark for robust and generalizable abdominal multi-organ segmentation research.
